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INDONESIA
International Journal of Intelligent Systems and Applications in Engineering
Published by Ismail SARITAS
ISSN : 21476799     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Intelligent Systems and Applications in Engineering (IJISAE) is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. IJISAE, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.
Arjuna Subject : -
Articles 200 Documents
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A Robust Adaptive Control of Interleaved Boost Converter with Power Factor Correction in Wind Energy Systems Karik, Fatih; Yildiz, Ceyhun; Kaytez, Fazil
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 1 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017526692

Abstract

Power converters are generally utilized to convert the power from the wind sources to match the load demand and grid requirement to improve the dynamic and steady-state characteristics of wind generation systems and to integrate the energy storage system to solve the challenge of the discontinuous character of the renewable energy. In the low-voltage wind energy systems, interleaved boost converters (IBC) are often used to operate high currents in the system. IBCs are extremely sensitive to the constantly changing loading conditions. These situations require a robust control operation which can ensure a sufficient performance of the IBC over a large-scale changing load. Neural networks (NN) have emerged over the years and have found applications in many engineering fields, including control. In this paper, the adaptive control of interleaved boost converter with power factor correction (PFC) is investigated for grid-connected synchronous generator of wind energy system. For this purpose, a model reference adaptive control (MRAC) based on NN is proposed. Analysis results show that the proposed control strategy for the IBCs achieves near unity power factor (PF) and low total harmonic distortion (THD) in a wide operating range.
Design and Implementation of High Speed Artificial Neural Network Based Sprott 94 S System on FPGA Koyuncu, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.97824

Abstract

FPGA-based embedding system designs have been preferred for industrial applications and prototyping because of the advantages of parallel processing, reconfigurability and low cost. Due to having characteristic structure of the parallel processing of Artificial Neural Networks (ANNs), these systems provide the advantage of speed and performance when they are implemented with FPGA-based hardware. The hardware implementation of transfer functions used for modeling non-linear systems is a challenging problem. Therefore, this problem creates convergence problems. In this paper, non-linear Sprott 94 S system has been modeled using ANNs running on FPGA. All related parameter values and processes are defined with IEEE-754-1985 32-bit floating point number format. ANN-based Sprott 94 S system design has been developed using VHDL synthesized using Xilinx ISE Design Tools. In test stage, ANN-based Sprott 94 S system has been tested using 3X100 data set and obtained error analysis results have been presented.  The constructed design has been performed for Xilinx VIRTEX-6 family XC6VHX255T-3FF1923 FPGA chip using Place&Route process and chip usage statistics have been given. The clock frequency of ANN-based Sprott 94 S system which has pipeline processing scheme has been obtained with the value of 304.534 MHz. Accordingly, the proposed FPGA-based ANN system has produced 3X3.284 billion outputs in 1 second.
DATA ANALYTICS OF BUILDING AUTOMATION SYSTEMS: A CASE STUDY DOGAN, GULUSTAN
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 2 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018642071

Abstract

In today’s technology, when costs of time, energy and human resources are considered, efficient use of resources provides significant advantages over many aspects. In light of this, role of building automation systems, which are a part of smart cities, become even more important. At the very core of building automation systems there lies the efficient use of resources and systems for providing comfortable living situations. With the advancement in network technology, systems can be programmed smartly and any malfunctions on the systems can be detected and fixed remotely. In addition to that, all data gathered during this process can be analyzed to create machine learning solutions for a system to control and program itself. In this work, we pulled the sensor data and developed an interface to do analysis. Our aim is to understand how the system behaves. This interface will be the basis of our work on developing machine learning algorithms to predict system behaviour for programming the system for energy.
Atmospheric and light-induced effects in nanostructured silicon deposited by capacitively and inductively-coupled plasma Saleh, Zaki Mohammad; Nogay, Gizem; Ozkol, Engin; Turan, Rasit
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.13913

Abstract

Renewable sources of energy have demonstrated the potential to replace much of the conventional sources but the cost continues to pose a challenge. Efforts to reduce cost involve highly efficient and less expensive materials as well as enhanced light management. Nanostructured materials consisting of silicon quantum dots in a matrix of amorphous silicon (a-Si) are promising for higher efficiency and better stability. Quantum confinement offers a tunable band gap, relaxes momentum conservation rule, and may permit multi exciton generation, MEG. We employ electron spin resonance (ESR), the temperature dependence of dark and photoconductivity to compare the stability of amorphous and nanostructured silicon films deposited by inductively- and capacitively-coupled plasma against atmospheric and light exposure. Distinctly different behaviors are observed for amorphous and nanostructured films suggesting that nanostructured films are more permeable to oxygen infusion but more resistant to light induced effect
CLASSIFICATION OF LEAF TYPE USING ARTIFICIAL NEURAL NETWORKS Yasar, Ali; Saritas, Ismail; Sahman, M. Akif; Dundar, A. Oktay
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 4 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.49279

Abstract

A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. Then Silva et al in 2014 have presented database comprises 40 different plant species. We performed in our study a classification process using dataset and artificial neural networks which have been prepared by Silva and et al. It has been determined that classification accuracy is over 92%.
A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons Beyranvand, Peyman; Kucuktezcan, Cavit Fatih; Cataltepe, Zehra; Genc, Veysel Murat Istemihan
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637931

Abstract

In this study, the effect of feature selection methods on the performance of multi-layer perceptrons used for the dynamic security assessment of electric power systems is investigated. The existence of many measurable parameters (features) characterizing the power system security status complicates the use of multi-layer perceptron both in terms of prediction accuracy and training time. In this paper, the dynamic security of a power system subject to a number of critical contingencies is assessed as the critical clearing time of any credible fault is predicted by a multi-layer perceptron. In addition to the study of two different feature selection methods, which are Minimum Redundancy Maximum Relevance (mRMR), and Regressional ReliefF (RReliefF), a novel multi-layer perceptron based feature selection method is proposed to be applied in the prediction of security indices. The performance of the feature selection methods on the dynamic security assessment is investigated on a 16-generator, 68-bus test system.
Fraud Detection on Financial Statements Using Data Mining Techniques Sorkun, Murat Cihan; Toraman, Taner
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 3 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017531428

Abstract

This study explores the use of data mining methods to detect fraud for on e-ledgers through financial statements. For this purpose, data set were produced by rule-based control application using 72 sample e-ledger and error percentages were calculated and labeled. The financial statements created from the labeled e-ledgers were trained by different data mining methods on 9 distinguishing features. In the training process, Linear Regression, Artificial Neural Networks, K-Nearest Neighbor algorithm, Support Vector Machine, Decision Stump, M5P Tree, J48 Tree, Random Forest and Decision Table were used. The results obtained are compared and interpreted.
About a discussion ‘‘Development a new mutation operator to solve the Traveling Salesman Problem by aid of genetic algorithms’’, by Murat Albayrak and Novruz Allahverdi, (2011). Expert System with Applications (38) (pp. 1313–1320) Allahverdi, Novruz
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the Short Communication published in “Expert Systems with Application” in volume 41 2014, (Comments on "Albayrak, M., & Allahverdi N. (2011). Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms. Expert Systems with Applications, 38(3), 1313-1320": A Proposal of Good Practice; E. Osaba, E. Onieva, F. Diaz, R. Carballedo, Volume: 41, Issue: 4, Pages: 1530-1531, Part: 1, MARCH 2014) the Osoba E. et al have discussed our method to solve the Traveling Salesman Problem pointing that we use our developed new algorithm to compare different versions of a classical genetic algorithm, each of one with a different mutation operator and they write that this can generate some controversy. Here we shortly analyze the comment of Osaba E. et al to show that our comparing method has a chance of existence.
Analysis and Validation of medical Application through Electrical Impedance based System Kumar, Ramesh; Kumar, Sharwan; Gupta, A.
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637925

Abstract

This paper discussed the Design and implementation of the electrical impedance-based system, which covers both techniques (Impedance Plethysmography (IPG) and Electrical Impedance Tomography (EIT)). The electrical impedance distribution image of the cross section of a phantom based on current excitation and voltage measurement using electrodes pair is reconstructs image according to the electrical property of the medical phantom. The electrical impedance based image provides the significant morphological information. Image quality depends upon many other iterative process and image processing algorithms in addition to.  The hardware designing of the system is a most important part for all impedance-based techniques. In measurement section, the current source of the electrical impedance-based system should supply multi-frequency signal for measurement because it provides more useful information about the phantom.  Therefore, a signal source that provides accurate excitation and a reference signal for measurement are more useful.  Matlab is used for image reconstruction and processing of the impedance data.
A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data Karaoglu, Sercan; Arpaci, Ugur; Ayvaz, Serkan
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31421

Abstract

Expert systems for trading signal detection have received considerable attention in recent years. In financial trading systems, investors’ main concern is determining the best time to buy or sell a stock. The trading decisions are often influenced by the emotions and feelings of the investors. Therefore, investors and researchers have aimed to develop systematic models to reduce the impact of emotions on trading decisions. Nevertheless, the use of algorithmic systems face another problem called “lack of dynamism”. Due to dynamic nature of financial markets, trading robots should quickly learn and adapt as human traders. Recently, a solution for detecting trading signals based on a dynamic threshold selection was proposed. In this study, we extend this approach by adopting several different rule based systems and enhancing it by using the Recurrent Neural Network algorithm. Recurrent Neural Networks learn the connection weights of subsystems with arbitrary sequences of inputs that make them a great fit for time series data. Our model is based on Piecewise Linear Representation and Recurrent Neural Network with the goal of detecting potential excessive movements in noisy stream of time series data. We use an exponential smoothing technique to detect abnormalities. Trading signals are produced using fixed time interval data from Istanbul Stock Exchange. The evaluations indicated that our model produces successful results in trading data. Future work will focus on further improvements and scalability of the model.

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